Nature子刊：上海科学智能研究院漆远/曹风雷/徐丽成团队开发新型AI模型，用于化学反应性能预测和合成规划

Core Viewpoint - Artificial Intelligence (AI) has significantly transformed the field of precise organic synthesis, showcasing immense potential in predicting reaction performance and synthesis planning through data-driven methods, including machine learning and deep learning [2][3]. Group 1: Research Overview - A recent study published in Nature Machine Intelligence introduces a unified pre-trained deep learning framework called RXNGraphormer, which integrates Graph Neural Networks (GNN) and Transformer models to address the methodological discrepancies between reaction performance prediction and synthesis planning [3][5]. - The RXNGraphormer framework is designed to collaboratively handle both reaction performance prediction and synthesis planning tasks through a unified pre-training approach [5][7]. Group 2: Performance and Training - The RXNGraphormer model was trained on 13 million chemical reactions and achieved state-of-the-art (SOTA) performance across eight benchmark datasets in reaction activity/selectivity prediction and forward/reverse synthesis planning, as well as on three external real-world datasets [5][7]. - Notably, the chemical feature embeddings generated by the model can autonomously cluster by reaction type in an unsupervised manner [5].